Hierarchical nucleus segmentation in digital pathology images

Yi Gao; Vadim Ratner; Liangjia Zhu; Tammy Diprima; Tahsin Kurc; Allen Tannenbaum; Joel Saltz

doi:10.1117/12.2217029

23 March 2016 Hierarchical nucleus segmentation in digital pathology images

Yi Gao, Vadim Ratner, Liangjia Zhu, Tammy Diprima, Tahsin Kurc, Allen Tannenbaum, Joel Saltz

Proceedings Volume 9791, Medical Imaging 2016: Digital Pathology; 979117 (2016) https://doi.org/10.1117/12.2217029
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Extracting nuclei is one of the most actively studied topic in the digital pathology researches. Most of the studies directly search the nuclei (or seeds for the nuclei) from the finest resolution available. While the richest information has been utilized by such approaches, it is sometimes difficult to address the heterogeneity of nuclei in different tissues. In this work, we propose a hierarchical approach which starts from the lower resolution level and adaptively adjusts the parameters while progressing into finer and finer resolution. The algorithm is tested on brain and lung cancers images from The Cancer Genome Atlas data set.

Citation Download Citation

Yi Gao, Vadim Ratner, Liangjia Zhu, Tammy Diprima, Tahsin Kurc, Allen Tannenbaum, and Joel Saltz "Hierarchical nucleus segmentation in digital pathology images", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 979117 (23 March 2016); https://doi.org/10.1117/12.2217029

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